Development Of Model Research Articles

Articular cartilage corefucosylation regulates tissue resilience in osteoarthritis.

This study aimed to investigate the glycan structural changes that occur before histological degeneration in osteoarthritis (OA) and to determine the mechanism by which these glycan conformational changes affect cartilage degeneration. An OA model was established in rabbits using mannosidase injection, which reduced high-mannose type N-glycans and led to cartilage degeneration. Further analysis of glycome in human OA cartilage identified specific corefucosylated N-glycan expression patterns. Inhibition of N-glycan corefucosylation in mice resulted in unrecoverable cartilage degeneration, while cartilage-specific blocking of corefucosylation led to accelerated development of aging-associated and instability-induced OA models. We conclude that α1,6 fucosyltransferase is required postnatally to prevent preosteoarthritic deterioration of articular cartilage. These findings provide a novel definition of early OA and identify glyco-phenotypes of OA cartilage, which may distinguish individuals at higher risk of progression.

Development and validation of a risk prediction model for preterm birth in women with gestational diabetes mellitus.

This study aims to develop and validate a prediction model for preterm birth in women with gestational diabetes mellitus (GDM). We conducted a retrospective study on women with GDM who gave birth at the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, between November 2017 and July 2021. We divided 1879 patients into a development set (n = 1346) and a validation set (n = 533). The development set was used to construct the prediction model for preterm birth using the stepwise logistic regression model. A nomogram and a web calculator were established based on the model. Discrimination and calibration were assessed in both sets. Patients were women with GDM. Data were collected from medical records. GDM was diagnosed with 75-g oral glucose tolerance test during 24-28 gestational weeks. Preterm birth was definied as gestational age at birth <37 weeks. The incidence of preterm birth was 9.4%. The predictive model included age, assisted reproductive technology, hypertensive disorders of pregnancy, reproductive system inflammation, intrahepatic cholestasis of pregnancy, high-density lipoprotein, homocysteine, and fasting blood glucose of 75-g oral glucose tolerance test. The area under the receiver operating characteristic curve for the development and validation sets was 0.722 and 0.632, respectively. The model has been adequately calibrated using a calibration curve and the Hosmer-Lemeshow test, demonstrating a correlation between the predicted and observed risk. This study presents a novel, validated risk model for preterm birth in pregnant women with GDM, providing an individualized risk estimation using clinical risk factors in the third trimester of pregnancy.

Experimental process characterisation for high-volume compression moulding of hybrid-architecture composites

Compression moulding of woven fabric prepreg and sheet moulding compound (SMC) in a single-shot process combines the superior mechanical properties of continuous fibre composites and the high design flexibility of discontinuous fibre composites. This experimental study presents insights into the moulding characteristics of prepreg and SMC and explores the interaction between these two materials during manufacturing of hybrid-architecture composites. All tests were performed under typical compression moulding conditions using a squeeze flow testing rig. The response of each material was interpreted through proposed material models applied to the data acquired from single architecture tests. Critical interaction mechanisms were identified from hybrid architecture tests, alongside some additional deformation mechanisms, such as high level of in-plane fibre tow deformation not observed in single-architecture tests. Novel experimental methods were also introduced to quantify the change in meso-scale fibre architecture in the prepreg induced by the flow of SMC. The outcomes of this study not only shed light on the essential interaction mechanisms of hybrid-architecture composites but also provide valuable insights into deformation mechanisms not apparent in isolated material studies. Ultimately, these findings will facilitate the future development of a numerical simulation model for hybrid moulding processes.

Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review.

The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies. To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice. MEDLINE, EMBASE and Cochrane Library were searched until June 2022. We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance. Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool). A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias. There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.

Articular cartilage corefucosylation regulates tissue resilience in osteoarthritis

This study aimed to investigate the glycan structural changes that occur before histological degeneration in osteoarthritis (OA) and to determine the mechanism by which these glycan conformational changes affect cartilage degeneration. An OA model was established in rabbits using mannosidase injection, which reduced high-mannose type N-glycans and led to cartilage degeneration. Further analysis of glycome in human OA cartilage identified specific corefucosylated N-glycan expression patterns. Inhibition of N-glycan corefucosylation in mice resulted in unrecoverable cartilage degeneration, while cartilage-specific blocking of corefucosylation led to accelerated development of aging-associated and instability-induced OA models. We conclude that α1,6 fucosyltransferase is required postnatally to prevent preosteoarthritic deterioration of articular cartilage. These findings provide a novel definition of early OA and identify glyco-phenotypes of OA cartilage, which may distinguish individuals at higher risk of progression.

From isotopically labeled DNA to fluorescently labeled dynamic pili: building a mechanistic model of DNA transport to the cytoplasmic membrane.

SUMMARYNatural competence, the physiological state wherein bacteria produce proteins that mediate extracellular DNA transport into the cytosol and the subsequent recombination of DNA into the genome, is conserved across the bacterial domain. DNA must successfully translocate across formidable permeability barriers during import, including the cell membrane(s) and the cell wall, that are normally impermeable to large DNA polymers. This review will examine the mechanisms underlying DNA transport from the extracellular space to the cytoplasmic membrane. First, the challenges inherent to DNA movement through the cell periphery will be discussed to provide context for DNA transport during natural competence. The following sections will trace the development of a comprehensive model for DNA translocation to the cytoplasmic membrane, highlighting the crucial studies performed over the last century that have contributed to building contemporary DNA import models. Finally, this review will conclude by reflecting on what is still unknown about the process and the possible solutions to overcome these limitations.

Numerical Study of Damage Accumulation in Brittle Spheres During Repeated Impacts and Development of a Conceptual Model for Rocks

Numerical Study of Damage Accumulation in Brittle Spheres During Repeated Impacts and Development of a Conceptual Model for Rocks

Estimation of soil health in the semi‑arid regions of northwestern Iran using digital elevation model and remote sensing data.

Nowadays, neglecting soil conservation issues is one of the most critical factors in reducing soil health (SH). In this regard, to facilitate the estimation of the SH in northwestern Iran, 292 soil samples were taken from a depth of 0-30cm of this area, and a wide range of soil properties were determined. Then, soil health indices (SHIs) were calculated. Simultaneously, the normalized difference vegetation index (NDVI), surface water capacity index (SWCI), and a digital elevation model (DEM) were obtained from satellite data. Finally, multiple linear regression (MLR) relationships between these parameters and SHIs were calculated. In this study, there was a highest significant positive correlation (P < 0.01) between IHI-LTDS and SWCI (0.71**), DEM (0.76**), and NDVI (0.73**). The MLR, with both the whole total (TDS) and minimal (MDS) dataset methods, which includes the aforementioned indices, strongly described the spatial variability of the Integrated Soil Health Index (IHI) (R2 = 0.78, AIC = - 416, RMSE = 0.05, and ρc = 0.76). According to the results of this study, it can be said that the development of SH estimation models using remote sensing extracted parameters can be one of the effective ways to reduce the cost and time of soil sampling in extensive areas.

Thrombosis in Acute Promyelocytic Leukemia: The Current Understanding.

Despite enormous improvement in the management of patients with acute promyelocytic leukemia (APL), the distinctive coagulopathy observed at presentation in affected patients is often life-threatening. While hemorrhagic manifestations are well known and described in this setting, APL-related thromboses are underappreciated. Data regarding this complication are scarce showing variable incidence. Furthermore, risk factors for thrombosis are inconsistent and unreliable; so, differentiation of increased risk of hemorrhage from an increased thrombotic risk is quite difficult in the absence of adequate predictive scores. Besides, prophylactic use of anticoagulants and recombinant thrombomodulin are a matter of ongoing debate. Also, due to the common feature of thrombocytopenia and other hemorrhagic risks, patients with APL are excluded from trials analyzing anticoagulant prophylaxis in cancers; so, data from prospective trials are lacking. A detailed analysis of thrombotic risks in APL with the development of a reliable risk stratification model is needed to further improve the care of APL patients.

ILOSCAR: interactive Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir model v1.0

Computationally inexpensive carbon cycle models serve as critical and efficient tools for illuminating the complex dynamics of the carbon cycle and its interplay with the climate system, offering insights into how our planet has responded to climate perturbations throughout its history. During geologic hyperthermal events, carbon cycle models are employed to trace the trajectory of carbon emissions and establish a connection between the emission trajectory and changes in the Earth's surface environment. To date, the prevalent method to estimate the carbon emission rate relies on coupling the carbon cycle modeling and proxy reconstructions. Most previous studies employ a forward methodology, i.e., they force the model with an array of predefined carbon injection scenarios and select the one that produces the best fit to one or more specific proxy-derived records (e.g., atmospheric pCO2, sea surface pH, and calcite compensation depth) as the optimal solution. However, this forward method has two potential disadvantages. First, it can be computationally expensive, particularly when tens of thousands of scenarios need to be conducted to find the best solution. Second, it might not yield the best injection trajectory if none of the predefined carbon emission curves represents the realistic emission curve. Hence, an inverse model that can reconstruct the carbon emission trajectory directly from the record/records is urgently needed. In this study, building upon the Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir (LOSCAR) Model (Zeebe, 2012), we develop an interactive carbon cycle model (named iLOSCAR) using the open-source Python language and include two options, a forward model and an inverse model. The forward model replicates the original LOSCAR model, while the inverse model calculates the emission trajectory constrained by the proxy records in a single run. Both models are accessible via a web-based interface, which allows users to interactively tune model parameters and conduct experiments. In this paper, we present the details of iLOSCAR, including model structure and derivation of key equations. We then validate iLOSCAR's performance through an identical twin test and model intercomparison. We also apply it to a climatic perturbation event to diagnose features of the emission pattern that were overlooked in previous studies. Finally, we discuss the possible directions for model's future development.

Efficient Usage of Energy Infrastructure in Smart City Using Machine Learning

The concept of smart cities revolves around utilizing modern technologies to manage and optimize city operations, including energy infrastructure. One of the biggest problems that smart cities have to deal with is ensuring the efficient usage of energy infrastructure to reduce energy consumption, cost, and environmental impact. Machine learning is a powerful tool that can be utilized to optimize energy usage in smart cities. This paper proposes a framework for efficient usage of energy machine learning for city infrastructure in smart cities. The proposed framework includes three main components: data collection, machine learning model development, and energy infrastructure optimization. The data collection component involves collecting energy consumption data from various sources, such as smart meters, sensors, and other IoT devices. The collected data is then pre-processed and cleaned to remove any inconsistencies or errors. The machine learning model development component involves developing machine learning models to predict energy consumption and optimize energy usage. The models can be developed using various techniques such as regression, classification, clustering, and deep learning. These models can predict energy consumption patterns based on historical data, weather conditions, time of day, and other factors. The energy infrastructure optimization component involves utilizing the machine learning models to optimize energy usage. The optimization process involves adjusting energy supply and demand to reduce energy consumption and cost. The optimization process can be automated, and SVM based machine learning models can continuously enhance their precision over time by studying the data. The proposed framework has several benefits, including reducing energy consumption, cost, and environmental impact. It can also improve the reliability and stability of energy infrastructure, reduce the risk of blackouts, and improve the overall quality of life in highly developed urban areas. Last but not least, the projected framework for efficient usage of energy machine learning for city infrastructure in smart cities is a promising solution to optimize energy usage and reduce energy consumption and cost. The framework can be implemented in various smart city applications, including buildings, transportation, and industrial processes.

Clinical development of MRI-based multi-sequence multi-regional radiomics model to predict lymph node metastasis in rectal cancer.

We aim to construct a magnetic resonance imaging (MRI)-based multi-sequence multi-regional radiomics model that will improve the preoperative prediction ability of lymph node metastasis (LNM) in T3 rectal cancer. Multi-sequence MRI data from 190 patients with T3 rectal cancer were retrospectively analyzed, with 94 patients in the LNM group and 96 patients in the non-LNM group. The clinical factors, subjective imaging features, and the radiomic features of tumor and peritumoral mesorectum region of patients were extracted from T2WI and ADC images. Spearman's rank correlation coefficient, Mann-Whitney's U test, and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Logistic regression was used to construct six models. The predictive performance of each model was evaluated by the receiver operating characteristic curve (ROC). The differences of each model were characterized by area under the curve (AUC) via the DeLong test. The AUCs of T2WI, ADC single-sequence radiomics model and multi-sequence radiomics model were 0.73, 0.75, and 0.78, respectively. The multi-sequence multi-regional radiomics model with improved performance was created by combining the radiomics characteristics of the peritumoral mesorectum region with the multi-sequence radiomics model (AUC, 0.87; p < 0.01). The AUC of the clinical model was 0.68, and the MRI-clinical composite evaluation model was obtained by incorporating the clinical data with the multi-sequence multi-regional radiomics features, with an AUC of 0.89. The MRI-based multi-sequence multi-regional radiomics model significantly improved the prediction ability of LNM for T3 rectal cancer and could be applied to guide surgical decision-making in patients with T3 rectal cancer.

Development of a case method learning model to hone students' translation competence in language teaching courses

In 2020-2024, the Ministry of Education and Culture launched one of the main objectives of developing a strategic plan, namely optimizing the quality of learning in innovative forms in order to realize student success in achieving maximum learning outcomes that include attitudes, knowledge and skills. Hodijah, S &amp; et al (2022) argue regarding the development goals of the Ministry of Education and Culture that the expected innovative learning can be implemented from learning created in the form of collaborative and participatory classes. Furthermore, according to the 2022 edition of EF English First Proficiency, Indonesia is in 81st place out of 111 countries with a score of 469 which falls into the Low Proficiency category in every aspect of the English language, one of which is Translation. Based on these things, to overcome the low level of students' English proficiency, especially in terms of Translation and to answer the demands of the Ministry of Education and Culture for Higher Education in creating innovative classes . It is recommended for lecturers to use the Case Study learning model because it has been proven to be effective in improving translation skills in various previous studies. Based on these things, this research aims to developCase Study Learning to Improve Students' Translation Ability in the Translation in English Language Teaching (ELT) Course, Department of English Language and Literature, Medan State University The development of the Case Study Learning Model was carried out using the ADDIE Development model which consists of 5 stages, namely; (1) Needs analysis, (2) Design of the Case learning model 3) Development of the Case Study Learning Model in Translation in ELT Learning (4) Implementation, namely trial of the Case Study learning model in the Translation in ELT course (5) Evaluation of Case Study model-based learning . This research uses a qualitative and quantitative descriptive approach in analyzing data. Validity tests, Practicality Tests and Effectiveness Tests was carried out on the development product to ensure that the Case Study Learning Model developed valid, practical and effective in improving students' translation skills in the Translation in ELT course. The results of the research show that the model developed is valid, practical and effective.

Optimized kernel extreme learning machine using Sine Cosine Algorithm for prediction of unconfined compression strength of MICP cemented soil.

Microbially induced calcite precipitation (MICP) is an eco-friendly bio-remediation technology. The unconfined compressive strength (UCS) of MICP cemented soil is an important indicator of repair effectiveness. This study proposes a machine learning technique utilizing the Sine Cosine Algorithm (SCA) to optimize the regularization coefficient C and kernel width γ of the kernel extreme learning machine (KELM) to predict the UCS of MICP cemented soil. To evaluate the performance of the proposed models, a dataset containing 180 groups of the UCS of MICP cemented soil was obtained. The results obtained by SCA-KELM were compared with those obtained by the Random Forest algorithm (RF), Support Vector Machine (SVM), and KELM. The performance of these models was evaluated by the scores of MAE, RMSE, and R2. The results indicate that the SCA-KELM algorithm exhibits optimal prediction performance (Total score: 21). After optimizing KELM with SCA, the total score improved by 110%, suggesting that SCA significantly enhances the KELM performance. After model development, the optimal population size for SCA-KELM was determined to be 50. Based on the mutual information test, an innovative method was developed for categorizing factor sensitivity by employing importance scores as the partitioning criterion. This method categorizes the influencing factors into three tiers: high (importance score: 8.03-11.14%), medium (importance score: 5.93-7.25%), and low (importance score: 3.23-5.18%). These results suggest that the proposed SCA-KELM algorithm can be regarded as a powerful tool for predicting the UCS of MICP cemented soil.

LEVERAGING AI AND DATA ANALYTICS FOR ENHANCING FINANCIAL INCLUSION IN DEVELOPING ECONOMIES

Financial inclusion, defined as the access and usage of financial services by all individuals and businesses, is critical for fostering economic development and reducing poverty, particularly in developing economies. However, significant portions of the population in these regions remain underserved or excluded from formal financial systems. Leveraging artificial intelligence (AI) and data analytics presents a promising avenue for addressing the challenges of financial exclusion and advancing financial inclusion in developing economies. This Review explores the role of AI and data analytics in enhancing financial inclusion. Firstly, AI technologies such as machine learning and natural language processing enable the analysis of vast datasets to identify patterns, behaviors, and creditworthiness of individuals and small businesses, thereby facilitating more accurate risk assessment and decision-making by financial institutions. Additionally, AI-powered chatbots and virtual assistants offer personalized financial guidance and support, improving accessibility to financial services for marginalized populations who may have limited literacy or access to traditional banking channels. Moreover, data analytics plays a crucial role in expanding financial inclusion by providing insights into customer preferences, spending habits, and transaction histories. By harnessing these insights, financial service providers can tailor their products and services to better meet the diverse needs of underserved communities, thereby increasing uptake and usage of formal financial services. Furthermore, data analytics enables the development of alternative credit scoring models that leverage non-traditional data sources such as mobile phone usage and utility payments, allowing individuals with limited credit histories to access credit on favorable terms. However, several challenges must be addressed to fully realize the potential of AI and data analytics in enhancing financial inclusion. These include concerns related to data privacy and security, ensuring the fairness and transparency of AI algorithms, and bridging the digital divide to ensure equitable access to technology-enabled financial services. In conclusion, leveraging AI and data analytics holds significant promise for enhancing financial inclusion in developing economies. By harnessing the power of these technologies, policymakers, financial institutions, and other stakeholders can work towards building more inclusive and resilient financial systems that empower individuals and businesses to participate more fully in the formal economy.&#x0D; Keywords: AI, Data Analytics, Financial Inclusion, Developing Economies, Leveraging.

Towards Mental Health Analysis in Social Media for Low-resourced Languages

The surge in internet use for expression of personal thoughts and beliefs has made it increasingly feasible for the social Natural Language Processing (NLP) research community to find and validate associations between social media posts and mental health status . Cross-sectional and longitudinal studies of low-resourced social media data bring to fore the importance of real-time responsible Artificial Intelligence (AI) models for mental health analysis in native languages. Aiming at classifying research for social computing and tracking advances in the development of learning-based models, we propose a comprehensive survey on mental health analysis for social media and posit the need of analyzing low-resourced social media data for mental health . We first classify three components for computing on social media as: SM - data mining/natural language processing on social media , IA - integrated applications with social media data and user-network modeling, and NM - user and network modeling on social networks. To this end, we posit the need of mental health analysis in different languages of East Asia (e.g., Chinese, Japanese, Korean), South Asia (Hindi, Bengali, Tamil), Southeast Asia (Malay, Thai, Vietnamese), European languages (Spanish, French) and the Middle East (Arabic). Our comprehensive study examines available resources and recent advances in low-resourced languages for different aspects of SM, IA, and NM to discover new frontiers as potential field of research.

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges.

As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data-intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support, while identifying security and privacy threats within each phase of the life cycle of such systems (ie, data collection, model development, and implementation phases). The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks. This study highlights the importance of addressing the security and privacy threats associated with generative AI in health care to ensure the safe and effective use of these systems. The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks associated with these systems. By examining the use cases and benefits of generative AI across diverse domains within health care, this paper contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. In addition, this study provides practical insights for stakeholders looking to adopt generative AI solutions within their organizations.

DEVELOPMENT OF COMPRESSIVE STRENGTH PREDICTIVE MODELS OF SELF-COMPACTING CONCRETE CURED USING DIFFERENT CURING METHODS

Curing is the process of controlling the rate and extent of moisture loss, relative humidity and temperature from newly poured concrete for a certain period of time after it has been cast or finished to ensure that the cement has been properly hydrated and the concrete has hardened. The concrete strength, durability and other physical properties are affected by curing and application of the various types as it relates to the prevailing weather conditions in a particular locality, as curing is one of many requirements for concrete production, as such it is important to study the effect of different curing method. The concrete cube specimens produced with cement, fine aggregate, and coarse aggregate mix-ratio of 1:2.23:1.62 were prepared with a water-cement ratio of 0.5 and superplasticizer (SP) dosages of 0.5%, 1.0%, and 1.5%. The SP dosages were computed as percentages by weight of the cement content. The cubes were tested for compressive strength after curing for 7, 14, 21, 28, and 56 days using three curing methods namely; Immersion, open air, and wet burlap curing methods. This study assessed the effect of different curing methods on compressive strength of self-compacting concrete through the development of a mathematical method to model and analyze the effect of the curing methods used on the compressive strength of the SCC and also to validate the reliability of the method used. Data Fit software was used in the model development, the curing age and super-plasticizer dosage were used as independent variables while the compressive strength...

Development and validation of a novel model to predict pulmonary embolism in cardiology suspected patients: A 10-year retrospective analysis.

As there are no predictive models for pulmonary embolism (PE) in patients with suspected PE at cardiology department. This study developed a predictive model for the probability of PE development in these patients. This retrospective analysis evaluated data from 995 patients with suspected PE at the cardiology department from January 2012 to December 2021. Patients were randomly divided into the training and validation cohorts (7:3 ratio). Using least absolute shrinkage and selection operator regression, optimal predictive features were selected, and the model was established using multivariate logistic regression. The features used in the final model included clinical and laboratory factors. A nomogram was developed, and its performance was assessed and validated by discrimination, calibration, and clinical utility. Our predictive model showed that six PE-associated variables (age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease). The area under the curve - receiver operating characteristic curves of the model were 0.721 and 0.709 (95% confidence interval: 0.676-0.766 and 0.633-0.784), respectively, in both cohorts. We also found good consistency between the predictions and real observations in both cohorts. In decision curve analysis, the numerical model had a good net clinical benefit. This novel model can predict the probability of PE development in patients with suspected PE at cardiology department.

Assessment of the bone biological age using multiple regression and neural network analysis

Background. Nowadays, many studies confirm the increased risk of osteoporosis in the elderly. Strategies for optimizing diagnosis include a combined use of various methods, so calculating bone biological age (BA) can also be important for disease prediction. Recently, some new methodological approaches to BA calculation with the mathematical methods implementation were proposed. However, studies utilizing advanced approaches, particularly neural network (NN) in Ukraine, are limited. The purpose of this study was to develop a bone BA model and compare its accuracy using stepwise multiple regression (MLR) and NN analysis methods. Materials and methods. In a single-center cohort study, we retrospectively analyzed the data of 3,071 healthy women aged 40–90 years. The analysis of the study indices included the anthropometric parameters, dual-energy X-ray absorptiometry indices (DXA), and the parameters of the 10-year probability of major osteoporotic fractures (FRAX). For the development of bone BA models, MLR methods were used and the construction of the NN model was performed using a multilayer perceptron model. Results. As a result of the calculation, we received the MLR formula for bone BA determining bone mineral density (BMD) of lumbar spine and radius, minimal femoral and hip BMD as well as Trabecular Bone Score, and FRAX. The MLR equation allowed to calculation of the bone BA with an error of less than 4.9 years for study and control groups and demonstrated the high connection between calculated and chronological (ChrA) ages (R = 0.77; p &lt; 0.00001). The use of NN analysis showed the best results using 6 input variables and 1 internal layer of 7 neurons. The assessment of the connection between BA and ChrA demonstrated a high coefficient of correlation (R = 0.88; p &lt; 0.000001) with an average error of age calculation of less than 3.7 years for the study and control groups. Conclusions. A comparison of the accuracy of both models in bone BA estimation revealed a significant advantage of the deep learning NN, however, the use trained NN model requires specialized software, whereas the MLR formula can be used without additional costs.